1 Prepare Transaction Data and Explore

We begin by loading our prepared AAVE transaction data into a dataframe. The dataset has over 400,000 rows, and 27 columns.

We are directly loading the dataframe from an Rds archive instead of a CSV file to conserve space.

#load Rds (binary version of csv file) into dataframe
# Assumes this notebook is in: ~/IDEA-Blockchain/DefiResearch/StudentNotebooks/Assignment02
df<-read_rds('../../Data/transactions.Rds')

# Let's take a quick look at the first few observation
head(df)

Now look at the summaries to see the types, values, and missingness (NA’s) of the data.

summary(df)
##      amount            borrowRate       borrowRateMode     onBehalfOf       
##  Min.   :        0   Min.   :    0.0           :386542   Min.   :2.578e+33  
##  1st Qu.:       24   1st Qu.:    3.3   Stable  : 18408   1st Qu.:4.174e+47  
##  Median :     1427   Median :    3.9   Variable: 76569   Median :7.522e+47  
##  Mean   :   191103   Mean   :    9.5                     Mean   :7.592e+47  
##  3rd Qu.:    24382   3rd Qu.:   10.8                     3rd Qu.:1.168e+48  
##  Max.   :600000000   Max.   :10002.0                     Max.   :1.461e+48  
##  NA's   :7289        NA's   :386542                      NA's   :7289       
##       pool              reserve         timestamp              user          
##  Min.   :9.862e+47   USDC   :105937   Min.   :1.607e+09   Min.   :2.578e+33  
##  1st Qu.:1.035e+48   WETH   :105279   1st Qu.:1.615e+09   1st Qu.:4.199e+47  
##  Median :1.035e+48   USDT   : 58266   Median :1.621e+09   Median :8.697e+47  
##  Mean   :1.034e+48   DAI    : 55211   Mean   :1.620e+09   Mean   :8.082e+47  
##  3rd Qu.:1.035e+48   LINK   : 26404   3rd Qu.:1.624e+09   3rd Qu.:1.173e+48  
##  Max.   :1.035e+48   WBTC   : 26344   Max.   :1.629e+09   Max.   :1.461e+48  
##                      (Other):104078                                          
##           type        reservePriceETH     reservePriceUSD    
##  borrow     : 94977   Min.   :1.000e+00   Min.   :0.000e+00  
##  deposit    :192006   1st Qu.:2.865e+14   1st Qu.:1.000e+00  
##  liquidation:  6289   Median :4.652e+14   Median :1.000e+00  
##  redeem     :126705   Mean   :3.458e+23   Mean   :6.774e+08  
##  repay      : 60542   3rd Qu.:9.411e+14   3rd Qu.:1.000e+00  
##  swap       :  1000   Max.   :1.647e+28   Max.   :4.252e+13  
##                       NA's   :7289        NA's   :7289       
##    amountUSD         collateralAmount  collateralReserve principalAmount  
##  Min.   :        0   Min.   :      0          :475230    Min.   :      0  
##  1st Qu.:       70   1st Qu.:      1   WETH   :  2665    1st Qu.:    962  
##  Median :     5836   Median :     14   LINK   :  1312    Median :   4362  
##  Mean   :   245851   Mean   :   5451   WBTC   :   686    Mean   :  66005  
##  3rd Qu.:    49871   3rd Qu.:    250   AAVE   :   333    3rd Qu.:  21533  
##  Max.   :754379487   Max.   :4638724   UNI    :   230    Max.   :4475668  
##  NA's   :7289        NA's   :475230    (Other):  1063    NA's   :475230   
##  principalReserve reservePriceETHPrincipal reservePriceUSDPrincipal
##         :475230   Min.   :1.000e+00        Min.   :    0.0         
##  USDC   :  2142   1st Qu.:4.062e+14        1st Qu.:    1.0         
##  USDT   :  1549   Median :4.682e+14        Median :    1.0         
##  DAI    :  1459   Mean   :1.556e+17        Mean   :  295.6         
##  GUSD   :   242   3rd Qu.:5.363e+14        3rd Qu.:    1.0         
##  TUSD   :   175   Max.   :4.203e+19        Max.   :83819.1         
##  (Other):   722   NA's   :475230           NA's   :475230          
##  reservePriceETHCollateral reservePriceUSDCollateral amountUSDPincipal
##  Min.   :1.000e+00         Min.   :0.000e+00         Min.   :      0  
##  1st Qu.:1.000e+00         1st Qu.:0.000e+00         1st Qu.:   1022  
##  Median :5.110e+14         Median :1.000e+00         Median :   4481  
##  Mean   :2.177e+21         Mean   :4.543e+06         Mean   :  67361  
##  3rd Qu.:1.110e+16         3rd Qu.:2.600e+01         3rd Qu.:  22066  
##  Max.   :9.116e+23         Max.   :2.509e+09         Max.   :4571839  
##  NA's   :475230            NA's   :475230            NA's   :475230   
##  amountUSDCollateral borrowRateModeFrom borrowRateModeTo  stableBorrowRate
##  Min.   :      0             :480519            :480519   Min.   :  0.0   
##  1st Qu.:      0     Stable  :   471    Stable  :   529   1st Qu.:  9.0   
##  Median :    476     Variable:   529    Variable:   471   Median : 10.9   
##  Mean   :  37060                                          Mean   : 11.7   
##  3rd Qu.:   7457                                          3rd Qu.: 12.0   
##  Max.   :5029023                                          Max.   :154.7   
##  NA's   :475230                                           NA's   :480519  
##  variableBorrowRate
##  Min.   :  0.0     
##  1st Qu.:  3.8     
##  Median :  3.9     
##  Mean   :  5.7     
##  3rd Qu.:  5.1     
##  Max.   :148.7     
##  NA's   :480519

First we’ll do some preliminary analysis before we ask detailed questions.

1.1 Analyze Transaction Types

Let’s examine the different types of transactions present in the data. We’ll make a simple bar plot to visualize the number of each transaction types. “Deposit” is the most common type of transaction, whereas “swaps” are the most rare.

#set color palette
colors = brewer.pal(6,"Set2")

#create barplot
barplot(table(df$type), main='Transaction Type Counts', xlab='Type',ylab='Count',col=colors)

There are more “deposits” than “borrows,” because users often need to overcollateralize for loans.

Now we’ll examine the amount of US dollars being used in the different types of transactions. We create box plots for the four types of transactions that have the “amount” feature associated with them, and we visualize the distribution of that column for the different transactions.

We can see that most transactions are completed with very little money.

#create boxplot
boxplot(amountUSD~type,data=df,outline=FALSE,col=colors,
        main="Transaction Amounts",xlab="Type",ylab="Amount (USD)")

We do find some very large amounts, so it’s helpful to look at this on a log scale.

boxplot(log(amountUSD)~type,data=df,outline=FALSE,col=colors,
        main="Log Transaction Amounts",xlab="Type",ylab="Log Amount (USD)")

Observation: There are many borrows and repays with high transactions amounts, but deposits and redeems have much lower transactions amounts.

1.2 Examine Reserve Coins

There are 50 different “Reserve” coins used in transactions in AAVE. Let’s create a table of those reserve coins with at least 500 transactions and rank order them by their volume.

# Use deplyr to drop NA reserves, add the counts and then kep only the top 20
reservecoins <- df %>%  drop_na(reserve) %>% 
  count(reserve) %>% 
  arrange(-n) %>% 
  head(20)

# Add the rank to help keep track of the reserve coins
reservecoins <- reservecoins %>% 
  mutate(rank=1:nrow(reservecoins),.before=reserve)

# List the results nicely with kable()
kable(reservecoins)
rank reserve n
1 USDC 105937
2 WETH 105279
3 USDT 58266
4 DAI 55211
5 LINK 26404
6 WBTC 26344
7 AAVE 12174
8 CRV 10593
9 UNI 7547
10 XSUSHI 7337
11 SNX 6938
12 SUSD 6542
13 6289
14 GUSD 6009
15 YFI 5919
16 BUSD 4863
17 TUSD 3317
18 BAL 3152
19 MKR 3101
20 REN 2638

Let’s look at the number of transactions types for each currency.

TopcoinSummary <- df %>% filter(reserve %in% reservecoins$reserve) %>%
  group_by(reserve) %>% 
  count(type) %>% 
  mutate(percent = n/sum(n)*100)

kable(TopcoinSummary)
reserve type n percent
liquidation 6289 100.0000000
AAVE borrow 2 0.0164285
AAVE deposit 7028 57.7295876
AAVE redeem 5141 42.2293412
AAVE repay 3 0.0246427
BAL borrow 215 6.8210660
BAL deposit 2171 68.8769036
BAL redeem 612 19.4162437
BAL repay 154 4.8857868
BUSD borrow 1685 34.6493934
BUSD deposit 1135 23.3395024
BUSD redeem 836 17.1910343
BUSD repay 1207 24.8200699
CRV borrow 1054 9.9499670
CRV deposit 5780 54.5643349
CRV redeem 2607 24.6105919
CRV repay 1152 10.8751062
DAI borrow 14133 25.5981598
DAI deposit 18552 33.6019996
DAI redeem 13381 24.2361124
DAI repay 8895 16.1109199
DAI swap 250 0.4528083
GUSD borrow 2282 37.9763688
GUSD deposit 1493 24.8460642
GUSD redeem 967 16.0925279
GUSD repay 1267 21.0850391
LINK borrow 1321 5.0030298
LINK deposit 15270 57.8321466
LINK redeem 8713 32.9987881
LINK repay 1097 4.1546735
LINK swap 3 0.0113619
MKR borrow 188 6.0625605
MKR deposit 1766 56.9493712
MKR redeem 986 31.7961948
MKR repay 159 5.1273783
MKR swap 2 0.0644953
REN borrow 196 7.4298711
REN deposit 1417 53.7149356
REN redeem 840 31.8423048
REN repay 183 6.9370735
REN swap 2 0.0758150
SNX borrow 433 6.2409916
SNX deposit 4002 57.6823292
SNX redeem 2052 29.5762468
SNX repay 451 6.5004324
SUSD borrow 1277 19.5200245
SUSD deposit 2403 36.7318863
SUSD redeem 1781 27.2240905
SUSD repay 1081 16.5239988
TUSD borrow 991 29.8763943
TUSD deposit 853 25.7160084
TUSD redeem 661 19.9276455
TUSD repay 796 23.9975882
TUSD swap 16 0.4823636
UNI borrow 567 7.5129190
UNI deposit 3912 51.8351663
UNI redeem 2540 33.6557573
UNI repay 527 6.9829071
UNI swap 1 0.0132503
USDC borrow 35469 33.4812200
USDC deposit 27586 26.0400049
USDC redeem 22131 20.8907181
USDC repay 20326 19.1868752
USDC swap 425 0.4011818
USDT borrow 22332 38.3276697
USDT deposit 12593 21.6129475
USDT redeem 10349 17.7616449
USDT repay 12719 21.8291971
USDT swap 273 0.4685408
WBTC borrow 2082 7.9031278
WBTC deposit 13994 53.1202551
WBTC redeem 8442 32.0452475
WBTC repay 1816 6.8934103
WBTC swap 10 0.0379593
WETH borrow 7234 6.8712659
WETH deposit 56373 53.5462913
WETH redeem 35505 33.7246744
WETH repay 6155 5.8463701
WETH swap 12 0.0113983
XSUSHI borrow 242 3.2983508
XSUSHI deposit 4382 59.7246831
XSUSHI redeem 2454 33.4469129
XSUSHI repay 259 3.5300532
YFI borrow 403 6.8085825
YFI deposit 2976 50.2787633
YFI redeem 2146 36.2561243
YFI repay 394 6.6565298

1.3 Look at Sample User Transaction Histories

Finally, we will examine the transaction history of different users. To do this, we will select 3 random users from the data who have completed between 100 and 300 transactions. Then, we create swarmplots displaying the different types of transactions those users made over time.

#set seed
set.seed(1)

# Select three random users that have between 100 and 300 transactions
users<-vector(length=3)
count<-0
while(count<=3){
  success<-FALSE
  while(!success){
    #get random user
    ruser<-sample(df$user,1)
    
    #check for valid number of transactions
    length<-nrow(filter(df,user==ruser))
    if (length>100 && length<300){
      users[count]=ruser
      success<-TRUE
      count<-count+1
    }
  }
}
df.rusers<-filter(df, user %in%users)

# Create a "swarmplot"

ggplot(df.rusers,aes(user, timestamp,color=type)) + 
        geom_beeswarm(cex=1)+
        coord_flip()+
        ggtitle("User Transaction History")

Observation: Users have very different transactions patterns, which we will try to better understand.

For readability in the future let’s add a column to the data with the datetimes of each transaction.

df <- df %>%
  mutate(datetime = as_datetime(timestamp))
stableBorrows <- df %>%
  filter(borrowRateMode == 'Stable') %>%
  select(amount, borrowRate, reserve, reservePriceETH, reservePriceUSD, amountUSD) %>%
  group_by(reserve) %>%
  summarise(meanBorrowRate = mean(borrowRate), meanAmountUSD = mean(amountUSD)) %>%
  arrange(-meanAmountUSD)

kable(stableBorrows)
reserve meanBorrowRate meanAmountUSD
WBTC 4.2390575 5.560887e+05
LINK 4.1593373 3.869544e+05
MKR 4.2695021 1.532603e+05
TUSD 14.7699809 7.857025e+04
USDT 13.2979463 7.522208e+04
USDC 12.2011867 7.125035e+04
DAI 14.3987054 6.952764e+04
MANA 4.4445942 6.839591e+04
BAT 5.6596412 5.655844e+04
REN 1.6789213 4.323582e+04
KNC 10.2808363 2.785920e+04
ZRX 4.4377931 1.898568e+04
ENJ 0.6129458 1.750630e+04
UNI 5.2818956 4.063297e-01
WETH 4.5540876 0.000000e+00 
ggplot(stableBorrows, aes(meanBorrowRate, meanAmountUSD)) + geom_point() + ggtitle("Stable Rate Borrows by Coin") + xlab("Average Borrow Rate (% APY)") + ylab("Average Amount Borrowed (USD)")

variableBorrows <- df %>%
  filter(borrowRateMode == 'Variable') %>%
  select(amount, borrowRate, reserve, reservePriceETH, reservePriceUSD, amountUSD, variableBorrowRate) %>%
  group_by(reserve) %>%
  summarise(meanBorrowRate = mean(borrowRate), meanAmountUSD = mean(amountUSD)) %>%
  arrange(-meanAmountUSD)
  
kable(variableBorrows)
reserve meanBorrowRate meanAmountUSD
PAX 1.4189667 759194.21381
YFI 25.0423153 579352.73415
USDC 7.3136164 465590.74771
WBTC 0.8969733 456755.49539
XSUSHI 0.4389817 430386.33101
DAI 9.2355330 354431.30876
SNX 16.6542886 309288.28960
USDT 7.5154919 306093.68839
SUSD 11.4928812 221874.51600
UNI 1.4402381 208870.57160
TUSD 14.5124541 169636.71608
AmmWBTC 7.6796791 153694.88693
ENJ 6.3437707 146525.18717
LINK 0.4668825 135880.07066
REN 4.3690875 135343.87096
AmmUSDT 11.6201854 124174.97422
AmmUSDC 11.7120704 117880.03931
RAI 21.0781242 110328.02862
RENFIL 9.5067321 104352.92464
CRV 49.9247441 104106.11557
MKR 6.0872225 99000.98792
BUSD 9.7180112 86287.81560
BAL 14.2020852 74815.48914
GUSD 7.0424511 54016.82953
KNC 21.4718490 48757.60156
MANA 6.9657417 48672.45181
BAT 6.1808241 27723.46389
AMPL 175.8169717 21739.83824
AmmDAI 8.6259297 21009.53690
ZRX 3.5022303 10874.09762
AAVE 0.0000000 2.17796
AmmWETH 9.8078071 0.00000
WETH 1.1912801 0.00000 
ggplot(variableBorrows, aes(meanBorrowRate, meanAmountUSD)) + geom_point() + ggtitle("Variable Rate Borrows by Coin") + xlab("Average Borrow Rate (% APY)") + ylab("Average Amount Borrowed (USD)")

allCoins <- df %>%
  group_by(reserve) %>%
  count(reserve) %>%
  arrange(-n)

kable(allCoins)
reserve n
USDC 105937
WETH 105279
USDT 58266
DAI 55211
LINK 26404
WBTC 26344
AAVE 12174
CRV 10593
UNI 7547
XSUSHI 7337
SNX 6938
SUSD 6542
6289
GUSD 6009
YFI 5919
BUSD 4863
TUSD 3317
BAL 3152
MKR 3101
REN 2638
ENJ 2457
MANA 1993
AmmWETH 1778
RAI 1532
AMPL 1497
AmmUSDC 1405
BAT 1377
KNC 1349
AmmDAI 1266
ZRX 905
AmmUSDT 825
AmmWBTC 344
AmmBptBALWETH 244
AmmBptWBTCWETH 159
AmmUniUSDCWETH 136
RENFIL 90
AmmUniUNIWETH 68
AmmUniWBTCWETH 57
AmmUniDAIWETH 52
AmmUniLINKWETH 43
AmmUniAAVEWETH 19
AmmUniCRVWETH 11
PAX 11
AmmUniWBTCUSDC 10
AmmUniSNXWETH 7
AmmUniRENWETH 6
AmmUniYFIWETH 6
AmmUniMKRWETH 5
AmmUniBATWETH 4
AmmUniDAIUSDC 3 
stableCoins <- df %>%
  filter(reserve == 'USDT' | reserve == 'USDC' | reserve == 'GUSD' | reserve == 'DAI' | reserve == 'WBTC' | reserve == 'SUSD' | reserve == 'TUSD') %>%
  group_by(reserve) %>%
  count(reserve) %>%
  arrange(-n)

kable(stableCoins)
reserve n
USDC 105937
USDT 58266
DAI 55211
WBTC 26344
SUSD 6542
GUSD 6009
TUSD 3317 
stableCoinTransactions <- df %>%
  filter(reserve %in% stableCoins$reserve) %>%
  group_by(reserve) %>% 
  count(type) %>% 
  mutate(percent = n/sum(n)*100)

kable(stableCoinTransactions)
reserve type n percent
DAI borrow 14133 25.5981598
DAI deposit 18552 33.6019996
DAI redeem 13381 24.2361124
DAI repay 8895 16.1109199
DAI swap 250 0.4528083
GUSD borrow 2282 37.9763688
GUSD deposit 1493 24.8460642
GUSD redeem 967 16.0925279
GUSD repay 1267 21.0850391
SUSD borrow 1277 19.5200245
SUSD deposit 2403 36.7318863
SUSD redeem 1781 27.2240905
SUSD repay 1081 16.5239988
TUSD borrow 991 29.8763943
TUSD deposit 853 25.7160084
TUSD redeem 661 19.9276455
TUSD repay 796 23.9975882
TUSD swap 16 0.4823636
USDC borrow 35469 33.4812200
USDC deposit 27586 26.0400049
USDC redeem 22131 20.8907181
USDC repay 20326 19.1868752
USDC swap 425 0.4011818
USDT borrow 22332 38.3276697
USDT deposit 12593 21.6129475
USDT redeem 10349 17.7616449
USDT repay 12719 21.8291971
USDT swap 273 0.4685408
WBTC borrow 2082 7.9031278
WBTC deposit 13994 53.1202551
WBTC redeem 8442 32.0452475
WBTC repay 1816 6.8934103
WBTC swap 10 0.0379593 
ammCoins <- df %>%
  filter(grepl('Amm', reserve))%>%
  group_by(reserve) %>%
  count(reserve) %>%
  arrange(-n)

ammCoinTransactions <- df %>%
  filter(reserve %in% ammCoins$reserve) %>%
  group_by(reserve) %>% 
  count(type) %>% 
  mutate(percent = n/sum(n)*100)
  
kable(ammCoinTransactions)
reserve type n percent
AmmBptBALWETH deposit 171 70.081967
AmmBptBALWETH redeem 73 29.918033
AmmBptWBTCWETH deposit 97 61.006289
AmmBptWBTCWETH redeem 62 38.993711
AmmDAI borrow 529 41.785150
AmmDAI deposit 289 22.827804
AmmDAI redeem 209 16.508689
AmmDAI repay 239 18.878357
AmmUniAAVEWETH deposit 11 57.894737
AmmUniAAVEWETH redeem 8 42.105263
AmmUniBATWETH deposit 3 75.000000
AmmUniBATWETH redeem 1 25.000000
AmmUniCRVWETH deposit 8 72.727273
AmmUniCRVWETH redeem 3 27.272727
AmmUniDAIUSDC deposit 2 66.666667
AmmUniDAIUSDC redeem 1 33.333333
AmmUniDAIWETH deposit 32 61.538461
AmmUniDAIWETH redeem 20 38.461539
AmmUniLINKWETH deposit 30 69.767442
AmmUniLINKWETH redeem 13 30.232558
AmmUniMKRWETH deposit 4 80.000000
AmmUniMKRWETH redeem 1 20.000000
AmmUniRENWETH deposit 3 50.000000
AmmUniRENWETH redeem 3 50.000000
AmmUniSNXWETH deposit 3 42.857143
AmmUniSNXWETH redeem 4 57.142857
AmmUniUNIWETH deposit 42 61.764706
AmmUniUNIWETH redeem 26 38.235294
AmmUniUSDCWETH deposit 82 60.294118
AmmUniUSDCWETH redeem 54 39.705882
AmmUniWBTCUSDC deposit 6 60.000000
AmmUniWBTCUSDC redeem 4 40.000000
AmmUniWBTCWETH deposit 37 64.912281
AmmUniWBTCWETH redeem 20 35.087719
AmmUniYFIWETH deposit 5 83.333333
AmmUniYFIWETH redeem 1 16.666667
AmmUSDC borrow 283 20.142349
AmmUSDC deposit 537 38.220641
AmmUSDC redeem 369 26.263345
AmmUSDC repay 216 15.373665
AmmUSDT borrow 180 21.818182
AmmUSDT deposit 297 36.000000
AmmUSDT redeem 209 25.333333
AmmUSDT repay 139 16.848485
AmmWBTC borrow 48 13.953488
AmmWBTC deposit 163 47.383721
AmmWBTC redeem 99 28.779070
AmmWBTC repay 34 9.883721
AmmWETH borrow 180 10.123734
AmmWETH deposit 1014 57.030371
AmmWETH redeem 473 26.602925
AmmWETH repay 111 6.242970 
unstableCoins <- df%>%
  filter(! reserve %in% stableCoins$reserve & ! reserve %in% ammCoins$reserve) %>%
  group_by(reserve) %>%
  count(reserve) %>%
  arrange(-n)

unstableCoinTransactions <- df %>%
  filter(reserve %in% unstableCoins$reserve) %>%
  group_by(reserve) %>% 
  count(type) %>% 
  mutate(percent = n/sum(n)*100)

kable(unstableCoinTransactions)
reserve type n percent
liquidation 6289 100.0000000
AAVE borrow 2 0.0164285
AAVE deposit 7028 57.7295876
AAVE redeem 5141 42.2293412
AAVE repay 3 0.0246427
AMPL borrow 438 29.2585170
AMPL deposit 601 40.1469606
AMPL redeem 301 20.1068804
AMPL repay 157 10.4876420
BAL borrow 215 6.8210660
BAL deposit 2171 68.8769036
BAL redeem 612 19.4162437
BAL repay 154 4.8857868
BAT borrow 158 11.4742193
BAT deposit 750 54.4662309
BAT redeem 335 24.3282498
BAT repay 132 9.5860566
BAT swap 2 0.1452433
BUSD borrow 1685 34.6493934
BUSD deposit 1135 23.3395024
BUSD redeem 836 17.1910343
BUSD repay 1207 24.8200699
CRV borrow 1054 9.9499670
CRV deposit 5780 54.5643349
CRV redeem 2607 24.6105919
CRV repay 1152 10.8751062
ENJ borrow 234 9.5238095
ENJ deposit 1302 52.9914530
ENJ redeem 681 27.7167277
ENJ repay 239 9.7273097
ENJ swap 1 0.0407000
KNC borrow 136 10.0815419
KNC deposit 643 47.6649370
KNC redeem 411 30.4670126
KNC repay 157 11.6382506
KNC swap 2 0.1482580
LINK borrow 1321 5.0030298
LINK deposit 15270 57.8321466
LINK redeem 8713 32.9987881
LINK repay 1097 4.1546735
LINK swap 3 0.0113619
MANA borrow 220 11.0386352
MANA deposit 1018 51.0787757
MANA redeem 563 28.2488710
MANA repay 192 9.6337180
MKR borrow 188 6.0625605
MKR deposit 1766 56.9493712
MKR redeem 986 31.7961948
MKR repay 159 5.1273783
MKR swap 2 0.0644953
PAX borrow 8 72.7272727
PAX deposit 2 18.1818182
PAX redeem 1 9.0909091
RAI borrow 352 22.9765013
RAI deposit 644 42.0365535
RAI redeem 326 21.2793734
RAI repay 210 13.7075718
REN borrow 196 7.4298711
REN deposit 1417 53.7149356
REN redeem 840 31.8423048
REN repay 183 6.9370735
REN swap 2 0.0758150
RENFIL borrow 29 32.2222222
RENFIL deposit 28 31.1111111
RENFIL redeem 11 12.2222222
RENFIL repay 22 24.4444444
SNX borrow 433 6.2409916
SNX deposit 4002 57.6823292
SNX redeem 2052 29.5762468
SNX repay 451 6.5004324
UNI borrow 567 7.5129190
UNI deposit 3912 51.8351663
UNI redeem 2540 33.6557573
UNI repay 527 6.9829071
UNI swap 1 0.0132503
WETH borrow 7234 6.8712659
WETH deposit 56373 53.5462913
WETH redeem 35505 33.7246744
WETH repay 6155 5.8463701
WETH swap 12 0.0113983
XSUSHI borrow 242 3.2983508
XSUSHI deposit 4382 59.7246831
XSUSHI redeem 2454 33.4469129
XSUSHI repay 259 3.5300532
YFI borrow 403 6.8085825
YFI deposit 2976 50.2787633
YFI redeem 2146 36.2561243
YFI repay 394 6.6565298
ZRX borrow 76 8.3977901
ZRX deposit 496 54.8066298
ZRX redeem 279 30.8287293
ZRX repay 53 5.8563536
ZRX swap 1 0.1104972 
borrowRateSwaps <- df %>%
  filter(type == 'swap') %>%
  select(borrowRateModeFrom, borrowRateModeTo, reserve, variableBorrowRate, stableBorrowRate, timestamp, user, onBehalfOf) %>%
  group_by(borrowRateModeFrom) %>%
  mutate(vsRatio = variableBorrowRate/stableBorrowRate)
variableSwaps <- borrowRateSwaps %>%
  filter(borrowRateModeFrom == 'Variable')

varToStable <- ggplot(variableSwaps, aes(variableBorrowRate, stableBorrowRate)) +
  geom_point(aes(color = variableSwaps$reserve)) +
  ggtitle("Swaps from Variable to Stable Borrows") +
  geom_abline(slope = 1, intercept = 0)
ggplotly(varToStable)
stableSwaps <- borrowRateSwaps %>%
  filter(borrowRateModeFrom  == 'Stable')

stableToVar <- ggplot(stableSwaps, aes(stableBorrowRate, variableBorrowRate)) +
  geom_point(aes(color = stableSwaps$reserve)) +
  ggtitle("Swaps from Stable to Variable Borrows") +
  geom_abline(slope = 1, intercept = 0)
ggplotly(stableToVar)
brsUSDT <- borrowRateSwaps %>%
  filter(reserve == 'USDT')%>%
  group_by(user) %>%
  mutate(vsRatio = variableBorrowRate/stableBorrowRate)

swapRatioOverTime <- ggplot(borrowRateSwaps, aes(as_datetime(timestamp), vsRatio)) +
  geom_point(aes(color = borrowRateModeFrom)) + 
  ggtitle("Variable/Stable Ratios Over Time (USDT)")

ggplotly(swapRatioOverTime)
borrowsUSDT <- df %>%
  filter(type=='borrow', reserve == 'USDT')

stableBorrowsUSDT <- borrowsUSDT %>%
  filter(borrowRateMode == 'Stable')

stableRatesOverTime <- ggplot(stableBorrowsUSDT, aes(as_datetime(timestamp), borrowRate)) +
  geom_point(aes(color = reserve)) + 
  ggtitle("Stable Borrow Rates Over Time (USDT)")

ggplotly(stableRatesOverTime)

Let’s take a look at the stable borrow rates of USDT as they vary from week to week:

variableBorrowsUSDT <- borrowsUSDT %>%
  filter(borrowRateMode == 'Variable')

varRatesOverTime <- ggplot(variableBorrowsUSDT, aes(as_datetime(timestamp), borrowRate)) +
  geom_point(aes(color = reserve)) + 
  ggtitle("Variable Borrow Rates Over Time (USDT)")

ggplotly(varRatesOverTime)